科研之友
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摘要
Image superresolution (SR) techniques have widely been used to satisfy the increasing resolution demands of many advanced applications in the remote sensing domain. However, most of the existing SR models are directly operated on the whole image without considering the deviations and different objective requirements of different regions in optical remote sensing images, which are not sensible enough. To fill this gap, we propose a novel saliency-oriented adaptive SR strategy motived by the visual attention mechanism. The key idea of this letter is employing diverse treatments on different regions according to their unique requirements. For instance, the reconstruction quality of regions of interest (ROIs) should be as fine as possible. First, we employ a saliency detection strategy based on the edge-enhancement discrete wavelet transform to generate a saliency map, which clearly demonstrates the distribution of ROIs. Then, with regard to these areas, a new SR strategy is applied to get a better performance, where the training process is upgraded with the feature optimization process. In addition, the rest regions also receive the standard A+ to improve the quality there. Finally, all restored high-resolution (HR) patches are fused together as the desired reconstructed HR image. The comparative experiments validate the effectiveness of our scheme.
- 出版日期2018-12
- 单位北京师范大学
